What question did this study set out to answer?

The aim is to compare the estimation methods of least squares and latent root regression.

February 10, 2026Technometrics

A Comparison of Least Squares and Latent Root Regression Estimators

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Authors

RGR. F. GunstSouthern Methodist University JWJ. T. WebsterSouthern Methodist University RMR. L. MasonSouthwest Research Institute

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Implication

Comparison reveals Latent Root Regression improves parameter estimation in the presence of multicollinearity, indicating a preference over least squares.

Key Points

The aim is to compare the estimation methods of least squares and latent root regression.
Comparison of least squares and latent root regression estimators
Analysis of parameter estimates affected by multicollinearity
Evaluation of estimation improvement based on parameter orientation
Latent Root Regression is preferred over least squares in multicollinear data
Improvement in estimation quality is orientation-dependent
Parameter and variable selection differences between the two methods are outlined

Cite This Study

Gunst et al. (1976) studied this question.

synapsesocial.com/papers/698a77afb312d1bda18c61d8 https://doi.org/10.1080/00401706.1976.10489403

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Implication

Comparison reveals Latent Root Regression improves parameter estimation in the presence of multicollinearity, indicating a preference over least squares.

A Comparison of Least Squares and Latent Root Regression Estimators

Discussion

Member takes

Implication

Key Points

Cite This Study

A Comparison of Least Squares and Latent Root Regression Estimators

Discussion

Member takes

Implication

Key Points

Cite This Study

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